Care first. Technology in service of it.
The most capable model in the world means nothing if it never reaches a patient. I work where engineering meets medicine — making sure powerful AI becomes real, safe, trusted care.
Close the research-to-practice gap
So much medical AI stalls in papers and prototypes. I focus on the harder, more valuable work: turning it into systems clinicians can actually use and rely on.
People before parameters
Every design decision traces back to a real person waiting for an answer. Safety, clarity, and trust come before novelty.
Built for the real world
Regulatory awareness, clinical constraints, and validation aren't afterthoughts — I design with them in mind from the first line of code.
Engineering into medicine
A biomedical engineering background across three degrees lets me carry an idea from research concept to a working, dependable system.
Projects
Open-source work spanning the AI stack — from deployed models to systems in active development. Each is a standalone repository.
Crafts
The techniques I reach for, where each earns its place in a clinical system, and the full toolset behind them.
Surgical Navigation
Intraoperative guidance with MRI-to-ultrasound registration and biomechanical modeling — built for real OR workflows.
Medical Imaging & Deep Learning
Segmentation, registration, and 3D pipelines across MRI, CT, and ultrasound.
RAG & LLM Agents
Retrieval systems and multi-agent orchestration for clinical knowledge and decision support.
GNNs & Physics-Informed AI
Graph learning constrained by physical law — anatomy and tissue modeled as structure, not flat features.
Simulation & FEA
Finite element tissue deformation for surgical planning and physics-based synthetic data.
Classical ML, RL & MLOps
Production pipelines, reinforcement learning for sequential decisions, and FDA-regulated deployment.
NLP & Signal Processing
Clinical NLP for EHR text and biosignal processing for medical-device data.
Medical Device Regulation
FDA SaMD pathways, 510(k)/PMA awareness, and clinical validation for AI-enabled devices.
Tools & technical skills
Certifications & continuing education
Ongoing coursework to stay current across the fast-moving parts of the AI stack — from agentic systems to cloud MLOps.
Reinforcement Learning Specialization
Four-course sequence: Fundamentals of RL, Sample-based Learning Methods, Prediction & Control with Function Approximation, and a capstone building a complete RL system.
AI for Medical Diagnosis
Built CNN models for image classification and segmentation to diagnose lung and brain disorders from medical scans.
TensorFlow Developer Professional Certificate
Introduction to TensorFlow, CNNs in TensorFlow, and NLP in TensorFlow — building scalable models for image and text data.
Develop Generative AI Applications: Get Started
Foundations of building applications on top of generative AI models.
AI Agent Fundamentals with Azure AI Foundry
Designing and deploying autonomous AI agents on Microsoft's Azure AI Foundry platform.
Foundations of AI & ML · Azure ML Pipelines · Azure Databricks
End-to-end model pipelines and data science workflows on Azure's cloud ML stack.
DevOps, DataOps, MLOps
Production practices for shipping and maintaining ML systems reliably at scale.
Data Engineering in AWS
Data gathering, missing-data handling, feature extraction and selection using PCA and variance thresholds.
Advanced Machine Learning and Deep Learning
Deeper architectures and training techniques beyond introductory ML.
Digital Twins · Mastering Digital Twins
Building virtual representations of physical systems for simulation-driven design and monitoring.
Innovate with ANSYS Simulation Tools
Applied simulation workflows using the ANSYS platform.
Machine Learning
Core ML theory and applied methods.
Conferences & professional events
Academic conferences from my Ph.D. research — each resulting in a peer-reviewed publication — alongside recent industry events tracking where AI, simulation, and medical-device regulation are heading.
Academic — Ph.D. research (all resulted in a publication)
IEEE EMBC — 44th Annual Intl. Conference of the IEEE Engineering in Medicine & Biology Society
Glasgow, Scotland. Presented nnU-Net-based multi-modality breast MRI segmentation research.
ANNSIM — Annual Modeling and Simulation Conference
San Diego, CA. Presented preoperative planning work for robotic breast surgery navigation. Best Paper, Medical Track.
ANNSIM — Annual Modeling and Simulation Conference
Hamilton, Ontario, Canada. Presented controlled-resolution breast meshing for FE-based surgical simulation.
ANNSIM — Annual Modeling and Simulation Conference
Complutense University of Madrid, Spain. Presented a deep-learning framework for breast cancer surgical navigation with intra-operative imaging.
Industry & professional development
World Agentic AI Summit — Luxatia International
Berlin, Germany. Two-day executive summit on autonomous AI systems, multi-agent architectures, and enterprise AI governance.
Simulation World Central — ANSYS
Minneapolis, MN. Industry event on advanced simulation across healthcare, automotive, and aerospace applications.
RAPS Twin Cities — MN Medical Devices Essentials
Medtronic Headquarters, Minneapolis, MN. Full-day regulatory symposium covering FDA submissions, biocompatibility, AI in MedTech, and EU MDR/IVDR.
Current Applications and Future of AI in Cardiology — Mayo Clinic
Napa, CA. CME course covering generative AI, predictive modeling, and clinical decision support in cardiology — imaging, NLP, model development, and regulatory pathways for clinical AI.
Background
I'm a biomedical engineer with a Ph.D. and a Senior AI/ML Engineer at Abbott, specializing in AI and machine learning for medical imaging and healthcare.
My doctoral research built a real-time, image-guided navigation system for breast cancer care, integrating deep learning, patient-specific modeling, and biomechanical simulation into one framework — from diagnosis through treatment planning, in collaboration with clinical teams.
My path spans three degrees earned across Cairo, Connecticut, and Virginia — and work across medical imaging, biomechanics, bioelectric engineering, and drug delivery. That breadth is what lets me translate research into clinical practice rather than leaving it in a paper.
Today I'm a Senior AI/ML Engineer at Abbott, applying that foundation to build AI that supports real patient care.
Senior AI/ML Engineer · Abbott
Building applied AI and machine learning for healthcare.
Ph.D., Biomedical Engineering
Old Dominion University, Norfolk, VA. Dissertation on real-time navigation for breast cancer surgery using neural networks. Advisor: Michel Audette, Ph.D.
M.S., Biomedical Engineering
University of New Haven, West Haven, CT.
B.E., Biomedical & Systems Engineering
Cairo University, Giza, Egypt.
Publications
Peer-reviewed conference work in medical imaging, breast MRI segmentation, and finite-element surgical simulation.
nnU-Net-based Multi-modality Breast MRI Segmentation and Tissue-Delineating Phantom for Robotic Tumor Surgery Planning
TLDR: A deep-learning pipeline segmenting multi-modality breast MRI with nnU-Net, paired with a tissue-delineating phantom, to support planning for robotic tumor surgery.
Multi-Modality Breast MRI Segmentation Using nnU-Net for Preoperative Planning of Robotic Surgery Navigation
TLDR: Extends multi-modality breast MRI segmentation to preoperative planning for robotic surgical navigation. Best Paper, Medical Track.
Multi-Material, Approach-Guided, Controlled-Resolution Breast Meshing for FE-Based Interactive Surgery Simulation
TLDR: A controlled-resolution, multi-material breast meshing method enabling finite-element-based interactive surgical simulation guided by the surgical approach.
Simulation of Breast Deformation Due to US Probe
TLDR: A biomechanical simulation modeling breast tissue deformation caused by ultrasound probe pressure — improving the accuracy of MRI-to-ultrasound registration for surgical navigation. Presented at ANNSIM 2025, Madrid, Spain.
Real-Time Navigation System for Breast Cancer Surgery with Pre- and Intra-Operative Imaging Using Neural Networks
TLDR: The full doctoral dissertation — an end-to-end AI-driven navigation system integrating deep learning, patient-specific modeling, and biomechanical simulation for breast cancer surgery, achieving 4.6 mm tumor localization.
Get in touch.
Always glad to connect with people working on AI in healthcare, research collaborators, and anyone curious about the work. Reach out anytime.